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首页> 外文期刊>International Journal of Environmental Research and Public Health >Assessment of the Eutrophication-Related Environmental Parameters in Two Mediterranean Lakes by Integrating Statistical Techniques and Self-Organizing Maps
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Assessment of the Eutrophication-Related Environmental Parameters in Two Mediterranean Lakes by Integrating Statistical Techniques and Self-Organizing Maps

机译：结合统计技术和自组织图对两个地中海湖泊富营养化相关环境参数的评估

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摘要

During the last decades, Mediterranean freshwater ecosystems, especially lakes, have been under severe pressure due to increasing eutrophication and water quality deterioration. In this article, we compared the effectiveness of different data analysis methods by assessing the contribution of environmental parameters to eutrophication processes. For this purpose, principal components analysis (PCA), cluster analysis, and a self-organizing map (SOM) were applied, using water quality data from two transboundary lakes of North Greece. SOM is considered as an advanced and powerful data analysis tool because of its ability to represent complex and nonlinear relationships among multivariate data sets. The results of PCA and cluster analysis agreed with the SOM results, although the latter provided more information because of the visualization abilities regarding the parameters’ relationships. Besides nutrients that were found to be a key factor for controlling chlorophyll-a (Chl - a), water temperature was related positively with algal production, while the Secchi disk depth parameter was found to be highly important and negatively related toeutrophic conditions. In general, the SOM results were more specific and allowed direct associations between the water quality variables. Our work showed that SOMs can be used effectively in limnological studies to produce robust and interpretable results, aiding scientists and managers to cope with environmental problems such as eutrophication.
机译：在过去的几十年中，由于富营养化和水质恶化，地中海淡水生态系统（尤其是湖泊）承受了巨大压力。在本文中，我们通过评估环境参数对富营养化过程的贡献，比较了不同数据分析方法的有效性。为此，使用了来自希腊北部两个跨界湖泊的水质数据，进行了主成分分析（PCA），聚类分析和自组织图（SOM）。由于SOM能够表示多元数据集之间的复杂和非线性关系，因此被认为是高级而强大的数据分析工具。 PCA和聚类分析的结果与SOM结果一致，尽管由于提供了有关参数关系的可视化功能，后者提供了更多信息。除了被认为是控制叶绿素a（Chl-a）的关键因素的营养素外，水温还与藻类生产成正相关，而Secchi圆盘深度参数却非常重要，与富营养条件呈负相关。一般而言，SOM结果更为具体，并且允许水质变量之间存在直接关联。我们的工作表明，SOM可以有效地用于语言学研究中，以产生可靠且可解释的结果，从而帮助科学家和管理人员应对富营养化等环境问题。

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